Human–agent collaboration for disaster response

Abstract

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked.

Search and rescue with autonomous flying robots through behavior-based cooperative intelligence

“…The Orchid disaster response system under development by the UK appears to be the closest to the approach described in this article (Ramchurn et al 2016). It uses decentralized control of a swarm of UAVs to enhance disaster rescue efforts.…”

Implementasi Automatic Waypoint untuk Return Trip pada Autonomous Robot dengan Titik Acuan Potensi Korban Bencana

“…Sementara itu, diperkuat oleh pernyataan Sarvapali Ramchurn dkk pada tahun 2016, bahwa operasi SAR bertujuan untuk menyelamatkan korban bencana yang masih hidup, dan waktu penyelamatan sangatlah penting. Reruntuhan di area bencana dapat memperlama proses penyelamatan tersebut, yang berakibat pada kegagalan dalam menyelamatkan korban bencana yang masih bertahan hidup (Ramchurn, 2016).…”

Global Mapping of Citizen Science Projects for Disaster Risk Reduction

“…Argentina (Le Coz et al, 2016), Australia (Madsen and O'Mullan, 2013;Yates and Partridge, 2015;Haworth et al, 2016;Hung et al, 2016;Zhong et al, 2016;Haworth, 2018), Belgium (Mossoux et al, 2016), Brazil (Marchezini et al, 2017;Hirata et al, 2018), Canada (Tappenden, 2015;Díaz et al, 2016;Rieger, 2016), Montserrat (Loughlin et al, 2002), Chile (Usón et al, 2016), China (Peng, 2017;Qi et al, 2017;Svensson, 2017), Colombia (Hermelin and Bedoya, 2008;Loaiza et al, 2017), Cambodia (Aalst et al, 2008), Costa Rica (Aalst et al, 2008), Czech Republic (Raška and Brázdil, 2015;Panek et al, 2017), Denmark (Frigerio et al, 2017), Democratic Republic of the Congo (De Albuquerque et al, 2016), Ecuador (Ibadango et al, 2007;Stone et al, 2014;Mothes et al, 2015;Armijos et al, 2017), Europe (Bossu et al, 2012;Wehn et al, 2015a;Maltoni et al, 2017). Finland (Frigerio et al, 2017), Global (Tapia et al, 2014;Bossu et al, 2016;Kong et al, 2016;Ramchurn et al, 2016;Jones et al, 2017;Ludwig et al, 2017), Grenada (Canevari-Luzardo et al, 2017), Haiti (Ghosh et al, 2011;...…”